Document Type : Original Article

Authors

1 Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran.

2 Department of Mathematics, University of Alabama at Birmingham, Birmingham, USA

3 Department of Management and International Business (MIB), University of Auckland, New Zealand

4 School of Management & Marketing, Taylor’s University, Malaysia.

Abstract

Purpose: Using the data envelopment analysis method to determine the most efficient companies on the Tehran Stock Exchange.
Methodology: In this study, three industries- banking, petrochemical, and pharmaceutical- were studied to determine efficient companies. Then, using the ideal planning method, the investment percentage of each company's share in the portfolio is calculated. This method adds the return and share risk as model variables and with another liquidity variable to them.
Findings: The results showed that after ranking the companies, their sensitivity can be analyzed by determining the weaknesses and recognizing the impact of variables to increase the efficiency of companies.
Originality/Value: Using the portfolio of efficient companies for investment leads to reducing investment risk and choosing the right portfolio.

Keywords

Banker, R. D., & Thrall, R. M. (1992). Estimation of returns to scale using data envelopment analysis. European journal of operational research, 62(1), 74-84.‏
Banker, R., Natarajan, R., & Zhang, D. (2019). Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis: second stage OLS versus bootstrap approaches. European journal of operational research, 278(2), 368-384.
Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial analysis journal48(5), 28-43.
Bowlin, W. F. (1999). An analysis of the financial performance of defense business segments using data envelopment analysis. Journal of accounting and public policy18(4-5), 287-310.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research2(6), 429-444.
Coelli, T., Rao, D. S. P., & Batteseo, G. E. (1998). An introduction to efficiency and productivity analysis. Kluwer academic publisher.
Dai, Z., & Wen, F. (2018). Some improved sparse and stable portfolio optimization problems. Finance research letters27, 46-52. DOI: 10.1016/j.frl.2018.02.026
Edalatpanah, S. A. (2018). Neutrosophic perspective on DEA. Journal of applied research on industrial engineering5(4), 339-345.
Edalatpanah, S. A. (2019). A data envelopment analysis model with triangular intuitionistic fuzzy numbers. International journal of data envelopment analysis7(4), 47-58.
Edalatpanah, S. A. (2020). Data envelopment analysis based on triangular neutrosophic numbers. CAAI transactions on intelligence technology, 5(2), 94-98.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the royal statistical society: series a (general)120(3), 253-281.
Kohl, S., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2019). The use of data envelopment analysis (DEA) in healthcare with a focus on hospitals. Health care management science22(2), 245-286.
Kalayci, C. B., Polat, O., & Akbay, M. A. (2020). An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm and evolutionary computation54, 100662. DOI: 10.1016/j.swevo.2020.100662
Kutin, N., Nguyen, T. T., & Vallée, T. (2017). Relative efficiencies of ASEAN container ports based on data envelopment analysis. The Asian journal of shipping and logistics33(2), 67-77.
Mao, X., Guoxi, Z., Fallah, M., & Edalatpanah, S. A. (2020). A neutrosophic-based approach in data envelopment analysis with undesirable outputs. Mathematical problems in engineering, 4, 1-8. DOI: 10.1155/2020/7626102
Perrin, S., & Roncalli, T. (2020). Machine learning optimization algorithms & portfolio allocation. Machine learning for asset management: new developments and financial applications, 261-328. DOI: 10.13140/RG.2.2.13566.95047
Rasoulzadeh, M., & Fallah, M. (2020). An overview of portfolio optimization using fuzzy data envelopment analysis models. Journal of fuzzy extension and applications1(3), 188-197.
Soltani, M. R., Edalatpanah, S. A., Sobhani, F. M., & Najafi, S. E. (2020). A novel two-stage DEA model in fuzzy environment: application to industrial workshops performance measurement. International journal of computational intelligence systems13(1), 1134-1152.
Toma, P., Miglietta, P. P., Zurlini, G., Valente, D., & Petrosillo, I. (2017). A non-parametric bootstrap-data envelopment analysis approach for environmental policy planning and management of agricultural efficiency in EU countries. Ecological indicators83, 132-143. DOI: 10.1016/j.ecolind.2017.07.049
Wu, Y., Xu, C., Ke, Y., Tao, Y., & Li, X. (2019). Portfolio optimization of renewable energy projects under type-2 fuzzy environment with sustainability perspective. Computers & industrial engineering133, 69-82. DOI: 10.1016/j.cie.2019.04.050
Yang, W., Cai, L., Edalatpanah, S. A., & Smarandache, F. (2020). Triangular single valued neutrosophic data envelopment analysis: application to hospital performance measurement. Symmetry12(4), 588.
Zhou, H., Yang, Y., Chen, Y., & Zhu, J. (2018). Data envelopment analysis application in sustainability: the origins, development and future directions. European journal of operational research264(1), 1-16.